README.md

metadata

tags:
  - setfit
  - sentence-transformers
  - text-classification
  - generated_from_setfit_trainer
widget:
  - text: >-
      Targeting benefits based on income and disability status allows for
      efficient allocation of resources to the most vulnerable children and
      families.
  - text: >-
      Encourage youth participation in the development, implementation,
      monitoring and 

      evaluation of comprehensive sexuality education programmes and youth
      friendly health  

      services; and 


      3.
  - text: |-
      The SM

      Es 
      have to m

      aintain the sam
      e level of em

      ploym
      ent during that period as to the 

      num
      ber insured under social security end-D

      ecem
      ber 2019.
  - text: |-
      There are challenges in the labour market 

      regarding realization of decent work for the majority of workers.
  - text: |-
      Causes and Consequences of Broad-Based Rural Poverty Reduction: Lessons 
      Learned 
       

      Stagnant levels of rural poverty pose a major challenge for Zambia.
metrics:
  - accuracy
  - f1_score
  - precision
  - recall
pipeline_tag: text-classification
library_name: setfit
inference: true
base_model: sentence-transformers/paraphrase-multilingual-mpnet-base-v2
model-index:
  - name: SetFit with sentence-transformers/paraphrase-multilingual-mpnet-base-v2
    results:
      - task:
          type: text-classification
          name: Text Classification
        dataset:
          name: Unknown
          type: unknown
          split: test
        metrics:
          - type: accuracy
            value: 1
            name: Accuracy
          - type: f1_score
            value: 1
            name: F1_Score
          - type: precision
            value: 1
            name: Precision
          - type: recall
            value: 1
            name: Recall

SetFit with sentence-transformers/paraphrase-multilingual-mpnet-base-v2

This is a SetFit model that can be used for Text Classification. This SetFit model uses sentence-transformers/paraphrase-multilingual-mpnet-base-v2 as the Sentence Transformer embedding model. A LogisticRegression instance is used for classification.

The model has been trained using an efficient few-shot learning technique that involves:

1. Fine-tuning a Sentence Transformer with contrastive learning.
2. Training a classification head with features from the fine-tuned Sentence Transformer.

Model Details

Model Description

• Model Type: SetFit
• Sentence Transformer body: sentence-transformers/paraphrase-multilingual-mpnet-base-v2
• Classification head: a LogisticRegression instance
• Maximum Sequence Length: 128 tokens
• Number of Classes: 2 classes

Model Sources

• Repository: SetFit on GitHub
• Paper: Efficient Few-Shot Learning Without Prompts
• Blogpost: SetFit: Efficient Few-Shot Learning Without Prompts

Model Labels

Label	Examples
Irrelevant	'Agri-business (Market access for agricultural products) \n\t4.' 'These are goals that translate into many programmes and policies, and countless \n\ninstitutional plans and activities.' 'Planning: developing synergies between different \ntypes of infrastructure that facilitate socio-economic \nintegration and the timely delivery of aid in crisis.'
Relevant	'Fiscal policies that sustain non-contributory family benefits ensure that the most disadvantaged children receive continuous support regardless of labor market fluctuations.' 'Social protection instruments that prioritize children living in poor households have a multiplier effect, positively influencing nutrition, education, and health indicators.' 'The expansion of benefits coverage to include all children, irrespective of socioeconomic status, underscores a commitment to universality and equitable social protection.'

Evaluation

Metrics

Label	Accuracy	F1_Score	Precision	Recall
all	1.0	1.0	1.0	1.0

Uses

Direct Use for Inference

First install the SetFit library:

pip install setfit

Then you can load this model and run inference.

from setfit import SetFitModel

# Download from the 🤗 Hub
model = SetFitModel.from_pretrained("faodl/model_child_and_family_support_benefits_mpnet_30_sample")
# Run inference
preds = model("There are challenges in the labour market 

regarding realization of decent work for the majority of workers.")

Training Details

Training Set Metrics

Training set	Min	Median	Max
Word count	2	25.3542	95

Label	Training Sample Count
Irrelevant	24
Relevant	24

Training Hyperparameters

• batch_size: (16, 16)
• num_epochs: (1, 1)
• max_steps: -1
• sampling_strategy: oversampling
• num_iterations: 20
• body_learning_rate: (2e-05, 2e-05)
• head_learning_rate: 2e-05
• loss: CosineSimilarityLoss
• distance_metric: cosine_distance
• margin: 0.25
• end_to_end: False
• use_amp: False
• warmup_proportion: 0.1
• l2_weight: 0.01
• seed: 42
• eval_max_steps: -1
• load_best_model_at_end: False

Training Results

Epoch	Step	Training Loss	Validation Loss
0.0083	1	0.1695	-
0.4167	50	0.0676	-
0.8333	100	0.0008	-

Framework Versions

• Python: 3.11.13
• SetFit: 1.1.2
• Sentence Transformers: 4.1.0
• Transformers: 4.52.4
• PyTorch: 2.6.0+cu124
• Datasets: 3.6.0
• Tokenizers: 0.21.1

Citation

BibTeX

@article{https://doi.org/10.48550/arxiv.2209.11055,
    doi = {10.48550/ARXIV.2209.11055},
    url = {https://arxiv.org/abs/2209.11055},
    author = {Tunstall, Lewis and Reimers, Nils and Jo, Unso Eun Seo and Bates, Luke and Korat, Daniel and Wasserblat, Moshe and Pereg, Oren},
    keywords = {Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences},
    title = {Efficient Few-Shot Learning Without Prompts},
    publisher = {arXiv},
    year = {2022},
    copyright = {Creative Commons Attribution 4.0 International}
}